Parking continuity with unused duration between automated vehicles

ABSTRACT

An artificial neural network trained to predict the availability of an unused duration of a parking space based on input features is executed. Input features may include at least a contextual situation associated with the second entity, a behavior factor associated with a first entity that has been using the parking space, geographical location and time, events occurring within a threshold distance from the parking space. The artificial neural network may be further trained to output a transfer affinity based on the predicted availability of an unused duration, the contextual situation associated with the second entity and the behavior factor associated with the first entity. Based at least on the transfer affinity, the second entity can be selected. The unused duration can be transferred to the second entity from the first entity. The transferring can also include storing a payment and associated computation as a blockchain node in a blockchain.

BACKGROUND

The present application relates generally to computer-implementedapparatus and method, which can facilitate transferring of unusedparking space durations between vehicles, for example, self-drivingautomated vehicles.

Increased presence of automated self-driven vehicles on the road alsoimplies increased need to allow those vehicles to be able to find andpark themselves in appropriate parking spots in an efficient manner.Generally, it is common to pay for parking at a pay station in a parkinglot. For example, one may enter a parking space number and pay for thatspace for a number of hours (or for another unit of time). In manyinstances, vehicles pay for more parking time duration than actuallyused, for example, so that the possibility of underestimating time andtherefore a penalty or fine, which may result, can be avoided. A vehiclethat leaves a parking spot before the full parking duration is reachedleaves behind an unused parking duration.

BRIEF SUMMARY

A system and method to facilitate transferring of unused parkingduration between vehicles is disclosed. A method in one aspect mayinclude predicting availability of an unused duration of a parking spacefor a second entity. Predicting may include executing acomputer-implemented, artificial neural network trained to predict theavailability of an unused duration of a parking space based on inputfeatures. Input features, in one aspect, may include at least acontextual situation associated with the second entity, a behaviorfactor associated with a first entity that has been using the parkingspace, geographical location and time, events occurring within athreshold distance from the parking space. The artificial neural networkmay be further trained to output a transfer affinity based on thepredicted availability of an unused duration, the contextual situationassociated with the second entity and the behavior factor associatedwith the first entity. The method may further include, based at least onthe transfer affinity, selecting the second entity. The method may alsoinclude transferring the unused duration to the second entity from thefirst entity. The transferring may further include storing a payment andassociated computation as a blockchain node in a blockchain.

A system in one aspect may include at least one hardware processor, amemory device coupled with the at least one hardware processor and acommunication interface coupled to the at least one hardware processor.A hardware processor may be operable to predict availability of anunused duration of a parking space for a second entity. Predicting theavailability may include executing a computer-implemented, artificialneural network trained to predict the availability of an unused durationof a parking space based on input features, which may include at least acontextual situation associated with the second entity, a behaviorfactor associated with a first entity that has been using the parkingspace, geographical location and time, events occurring within athreshold distance from the parking space. The artificial neural networkmay be further trained to output a transfer affinity based on thepredicted availability of an unused duration, the contextual situationassociated with the second entity and the behavior factor associatedwith the first entity. Based at least on the transfer affinity, ahardware processor may be operable to select the second entity. Ahardware processor may transfer the unused duration to the second entityfrom the first entity. A hardware processor may also store a payment andassociated computation as a blockchain node in a blockchain.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a computing system facilitating parking continuityaccording to an embodiment.

FIG. 2 illustrates an example implementation of determining unusedduration in one embodiment.

FIG. 3 is a flow diagram illustrating a method in one embodiment.

FIG. 4 is a diagram showing components of a system in one embodiment,which may implement parking continuity apparatus and/or method.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure.

DETAILED DESCRIPTION

A method, system and techniques may be provided to facilitatetransferring of unused parking spot duration from a primary entity(e.g., a manual or automatic self-driven vehicle (SDV)) to a secondaryentity (e.g., a manual or automatic self-driven vehicle) based oncontextual analysis. In one aspect, a method may include predicting andbroadcasting availability of an unused duration of a parking spot forone or more incoming secondary entities at a parking lot, analyzingtransfer decision factors (e.g., temporal context and cognitive state ofeach secondary entity, geo-spatial context) for the unused duration andpossibly for associated parking spot, computing a transfer request scorefor each incoming secondary entity based on analysis of transferringfactors and analysis of the primary entity context, and transferring theunused parking duration for a selected secondary entity based on thetransfer request score. A method is disclosed which facilitate vehicleor automobile parking and parking continuity in different aspects, andwhich may facilitate incentives (e.g., a payment, rating, token,fueling) between the primary and secondary entities may be alsoprovided.

In one aspect, a methodology of the present disclosure may improve crowdsource-based coordination of usage-driven and context-based parking lotservice, and in particular to techniques for predicting availability ofan unused duration of a parking spot and making available the unusedduration along with analysis of parking spot context and characteristicsfor one or more incoming secondary entities, determining one or morefactors influencing the decisions as to whom to transfer the unusedduration (e.g., the degree of “importance” for the secondary entity inparking within a given or selected time window) by exploiting temporalcontext and cognitive state, geo-spatial context, and/or other context.Some embodiments may utilize the factors that influenced the transfer todynamically adjust or structure an incentive for the primary entity orparking authorities that control multiple parking spots.

In another aspect, the determined or predicted unused duration amountassociated with a first entity (e.g., SDV) at a parking spot may beadjusted based on feedback data, for example, user feedback data. In afurther aspect, predicting the duration of stay at the specifiedlocation in context may involve correlating the identified location withhistorical data, the contextual situation, a schedule of events atfacilities in a region or area of the parking spot, and calendar entriesassociated with the event/location data. Still in another aspect, amethod may be provided which enables dynamically changing or convertinglanes on a street to parking spaces based on a current or a predictedcongestion level. For instance, based on detecting or predictingcongestion levels or traffic levels, an appropriate authority may benotified, which can close parts of a road or lane to be used as parkingspots, for example, dynamically on an as-needed basis. Yet in anotheraspect, a smart parking method for autonomous vehicle may be provided inwhich the autonomous vehicle can detect a temporary occupier such as astreet sweeper in the vicinity of a parking spot, and automaticallydetermine that the parking spot is only temporarily or intermittentlyunavailable for parking. In this case the autonomous vehicleautomatically drives around the area (e.g., around the block) andreturns to the parking spot (for example, giving enough time for thesweeper to clean the parking spot and move on). Heretofore the streetswould be closed for fixed periods of time to allow for the streetsweeper to clean the streets. In the event that the street sweeperfinished before the end of the fixed period of time, the parking spotson the street would not be available for parking.

In yet another aspect, a method may learn that a secondary entity (e.g.,SDV, non-SDV), also referred to as second entity, is heading to ormoving in a direction of the parking lot where the primary entity (alsoreferred to as first entity) has parked, based on the secondary entitydata which may be discovered by searching electronic calendar events,discovered by a geo-fencing zone passing, social media interactions,and/or other information, and trigger a determination, estimation orprediction of a primary entity's unused duration, and for example,initiate a transfer process. Based on learning or predicting that asecond entity is entering or heading toward a parking lot, a query orprediction may be triggered to determine unused durations associatedwith one or more first entities, for instance, predicted or determinedto leave the parking lot.

Yet in another aspect, a plan may be dynamically generated using smartcontracts and blockchain, a list of encrypted records or block andrelated technology for recording and accessing data. For example,incentives may be provisioned via blockchain to the secondary entitiesbased on user profiles and contextual situations of the secondaryentities and their desire to park at a parking spot. The desirability ofparking at a particular parking space may take into consideration theproximal distance of the user's predicted destination from therespective parking spots available and the parking time associated withthe space which may be favorable based on an estimated duration of stayof the respective users at a particular destination.

In a further aspect, the first entity (a user or SDV) may be interestednot only in exchanging unused parking duration but also additional dataabout the parking spot that may be useful for the secondary entity (adriver, an SDV or an occupant of SDV) to make a real-time decision(e.g., accept a generated incentive, or offer an alternative one, orpick a parking spot that is suitable for the current context orcondition, for instance, to select a parking spot near a train stationentrance to facilitate entry of a passenger). Additional data, which maybe exchanged, may include a photo of the parking spot, short videostream, inferred characteristic of the parking spot (e.g., puddles nextto the driver's side), computed proximity to building or structureentrances, location context, further time sensitiveness information,estimated risk or convenience, and/or others.

In yet another aspect, compensation or payment information inconjunction with the transfer charge and computation may be stored indiscrete blocks or nodes in the blockchain for all the respective userswho are/can be involved in transferring the unused time left at therespective parking spot in order for the user to later traverse throughthe set of transactions and cross-validate the payments executed by thesecondary entities while transferring the payment via the digitalcurrency.

FIG. 1 shows a computing system facilitating parking continuityaccording to an embodiment. One or more computer devices 108 may includevarious functions or components, which implement a methodology of thepresent disclosure according to embodiments. Components of a system 110may include computer-implemented components or functionality, forinstance, implemented and/or run on one or more hardware processors, orcoupled with one or more hardware processors. One or more hardwareprocessors 144, for example, may include components such as programmablelogic devices, microcontrollers, memory devices, and/or other hardwarecomponents, which may be configured to perform respective tasksdescribed in the present disclosure. Coupled memory devices may beconfigured to selectively store instructions executable by one or morehardware processors.

Components of a system 110 may be configured with one or more computingdevices 108 or communication devices 102. In some embodiments, an app(such as a smartphone app or application) may be implemented, which forexample, may execute on a communication device 108 and communicate withone or more components of the system 110, for example, executing on adevice 108 such as a server device, a virtual machine on a cloudcomputing system, and/or others. A computer process implementing amethod of parking continuity may be automatically triggered and run inthe background as a background computer process (e.g., an operatingsystem's daemon process), for example, on a user's device 102 and/or adevice 108, responsive to receiving an acknowledgment of a firstentity's payment for parking (e.g., at a parking lot, a municipal meter,or at another parking location), triggered based on user-specifiedrules, and/or triggered by scanning a parking ticket, for example, usingan app (e.g., the app associated with the system or another app thatcommunicates with the system) that runs an image recognition algorithm,or by scanning a Quick Response Code (QR code) of the parking ticket(e.g., if enabled), but not limited to such. Other triggering mechanismmay be implemented.

In one embodiment, the system 110 may receive and analyze miscellaneousdata sources 114 (e.g., “parking continuity data”) of a first entity 104or an occupant of the first entity's vehicle, a second entity 106 oroccupants of the second entity's vehicle, historical data 116 such ashistorical data of unused duration amounts for a similar location orparking lot, historical calendar data, historical behavioral data,profile of each entity, user or occupant, data from real-time monitoringof the user mobility devices (accelerometer, GPS, gyroscope, etc),historical communication means, geo-location, and/or others. Additionaldata received and considered may include weather and location contextdata 118 and interaction and call data 120. All or some of suchinformation may be provided or received from the first and secondentities (e.g., from devices associated with the first and secondentities) in an opt-in/opt-out basis.

In a similar opt-in and opt-out manner, the system may also receive andanalyze data from an entity's electronic calendar, for example,locations, durations and appointments. In another embodiment, the systemconsiders the time for events for establishments (e.g., commercialestablishments) or the like in the “vicinity” of the parking lots. Forinstance, event monitor functionality 122 may monitor events. Todetermine whether an event is occurring in the vicinity of a givenparking lot, a threshold distance factor may be preconfigured, and adistance (e.g., geographical distance) between the location of the eventand the parking lot and the threshold distance factor may be compared.If the distance between the location of the event and the parking lot isless than the threshold distance, it may be determined that the event isoccurring in the vicinity of the parking lot. Entities may typicallyarrive at a train station parking lot within 15 minutes before thedeparture of a train (an example of an event), and entities may exit theparking lot within 5 minutes after the arrival of a train (anotherexample of an event). Thus, the departure and arrival times of entitiesat a parking lot near a train station may tend to be discrete timeintervals. As another example, similar considerations can be implementedfor arrival and departure times in parking lots near concerts or othervenues depending on the start and end time of the concerts or othervenues.

A system in some embodiments may also provide a method for secondentities (e.g., vehicles) 106 to bid for spots from the first vehicle104. For instance, payment gateway functionality 124 may allow users tobid for spots and accept bids. A starting bid rate may be determinedbased on the remaining time and cost for that time, but may bemodifiable considering local traffic demand for spots and predictedavailability of the other spots from other primary vehicle owners.

Unused duration estimator functionality 112 may calculate, determineand/or predict unused duration. The unused duration may be monitored andcalculated by analyzing a first entity's location data such as a globalpositioning system (GPS) data and clock or current time. For instance, afirst entity's parked location and the time or arrival at the locationmay set the start of the parking duration. Analyzing a transaction(e.g., charge or recharge) may determine the amount of time that waspaid for, as there may be discrete charges for given amounts of time(e.g., 1 monetary unit for 1 hr, 2 monetary unit for 2 hrs).

In some embodiments, the parking continuity data is synthesized andanalyzed using one or more machine learning algorithms and building oneor more predictive models that can be used for detecting or predictingan unused duration amount of the parking ticket. The system may send thedetermined or predicted unused duration amount to the user or occupantof the first entity device (e.g., mobile phone or another device of thefirst entity) 102, for example, for further receiving feedback, gettingapproval, and/or otherwise communicating with the first entity 104regarding the determined or predicted unused duration amount. In someembodiments, based on the received feedback, the method of determiningor predicting unused duration amount is automatically adjusted.

The system may learn that a second entity 106 is heading to the parkinglot where the first entity 104 has parked based on the second entitydata (e.g., GPS data, calendar events, learned from conversation,discovered by a geo-fencing zone passing, social media interactions,and/or other data) and determine whether a first entity unused durationis available, e.g., at that parking lot or within a threshold distanceof where the second entity 106 is heading.

Usage of GPS data also enables marking of the parking spots utilized bythe first entity. In an opt-in/opt-out manner (for example, such thatany data pertaining to a user is determined or used based a user'spermission), a user's movement such as a user returning to the locationwhere the user's vehicle is parked can aid in predicting that the user'svehicle is ready to leave the parking spot. For example, such data cantrigger early notification of when users might be returning to a vehicleto increase the scheduling window of the parking space.

In a further instance, where a first entity 104 (a user or a self-drivenvehicle, SDV) may be interested not only to exchange unused parkingduration but also additional data about the parking spot that may beuseful for the second entity 106 (a driver, an SDV or an occupant ofSDV) to make a real-time decision (e.g., accept a generated incentive,or offer an alternative one, or picking a parking spot that is suitablefor their context or condition, for instance a spot near the trainstation entrance to facilitate the entry for a passenger).

For example, the data may include photos of the parking spot, a videostream, inferred characteristics of the parking spot (e.g., puddles nextto the driver's side), computed proximity to nearby entrances, locationcontext, further time sensitiveness information, estimated risk orconvenience, and/or others. For example, in one example scenario, thesystem may automatically detect and characterize the parking spot withrespect to road-surface risk (e.g., possible puddle, snow) orconvenience for an occupant of the incoming entity who may open the doorof the vehicle (e.g., a passenger of a SDV) and step out, for example,considering and quantifying at least one state variable relating to anoccupant (e.g., health state, high heels, and/or others). Suchconsideration may help, for example, in scenarios in which items such asa stroller or wheelchair or another item may need to be opened orhandled next to a parked vehicle.

Context dependent data such as location, proximity to main entrance,access to facilities, and/or others, may be considered, for instance,which data may affect a second entity's decision as to where to park thevehicle. For instance, an occupant of the second entity may need to beat a place (e.g., for a meeting or an appointment) within a limitedtime, and the distance between an available parking spot and an entranceto the place may influence the second entity's decision as to whichparking spot to use.

The system may analyze and determine context of the first entity, thefirst entity user or occupants, second entity, second entity user oroccupants using the context analyzer module. This module may comprisecurrent location analysis and approximate duration of stay at the saidlocation or parking spot based on at least second entity's calendarinformation, geo-spatial and temporal metrics, previous pattern history,etc. The results of the context analyzer may be consumed by the unusedduration estimator in order to compute the time of broadcast and furtherestimate the charge to be requested from the primary entity. In someembodiments, components or functionalities such user context analyzer126, as geo-spatial context analyzer 128, parking spot analyzer 130 andmobility/event analyzer 132 may function to provide data used indetermining unused duration of a suitable parking spot by an unusedduration estimator 112 for a second entity 106.

Transfer score estimator component or functionality 134 may compute atransfer score associated with a second entity 106. In some embodiments,transfer score estimator 134 may include a rectified linear unit (ReLU),sigmoid function or another function of user configurable parameters,which may include vectored inputs obtained from geo-spatial contextanalyzer 128, parking spot analyzer 130, mobility/event analytics 132and/or user context analysis engine 126. In some embodiments, a transferscore may be generated for each received requests of the determinedunused duration of a parking spot. In some embodiments, the transferscore may be generated based on the second entity 106, one or moreoccupants of the second entity 106 and/or other factors. For instance,based on the transfer score, a transfer modulation module orfunctionality 136 may dynamically select a second entity to transfer theunused duration. In some embodiments, the transfer modulation module orfunctionality 136 may, for example, via incentive generatorfunctionality 138 (also referred to as incentive generator) and paymentgateway functionality 124 (also referred to as payment gateway),structure a financial incentive, for example, based on the detectedparking spot characteristics and computed transfer score.

Incentive generator 138 may provision an incentive to a second entitybased on entity profile 140 (which may include user profile, e.g.,occupant of a vehicle) and contextual situation of the second entity andthe second entity's desire to park at a parking spot. The incentivegenerator 138 may perform its function for a plurality of secondentities. The desirability of parking at a particular parking space maybe determined based on the proximal distance of a user's (e.g., vehicleoccupant's) predicted destination from the parking spot available andthe parking time associated with the space, which may be consideredfavorable based on the estimated duration of stay of the respectiveuser's at a particular destination.

Data used as input to compute a user-parking spot affinity (e.g., interms of the temporal degree of desirability for a user or cohort) maybe obtained from geo-spatial temporal metrics store (e.g., which maystore data such as location profile such as information about alocation, weather data, temperature, context profile, sensed data viacameras, visual sensors, auditory or health sensors), spacing withrespect to other vehicles (some users may prefer to keep proper distancefrom other parked vehicles), other nearby computing or communicationdevices, and other data sources such as social media data (e.g., socialnetwork application data), and users' profiles. User profiles caninclude data such as use preferences, for example, parking close to atrain station, parking spots with abundant amount of light, and/orothers. In another aspect, user preferences can be dynamically learnedbased on pattern history and establishment of confidence level (rigidityfactor) over time as a factor for taking subsequent actions withouthuman intervention.

Blockchain logger functionality 150 (also referred to as blockchainlogger) may store payment information, e.g., with a transfer charge, andassociated computation in discrete blocks or nodes in the blockchain forall the respective users who are or can be involved in transferring theunused time left at the respective parking spot. The blocks or nodes canbe traversed to examine the set of transactions and cross-validate thepayments executed by secondary entities while transferring the paymentvia the digital currency. For instance, a first entity with unusedduration for a parking spot which is being transferred can be rewardedvia digital currency, for instance, on a social media portal, for thetime or duration the new user (second entity) will utilize the parkingspace, e.g., the amount of time that was left for the space and the timethe new user used the available space.

In addition, simultaneously for example, a rating of a parking space,e.g., provided by the respective entities associated with the nodes anddigital identifiers, can be stored with the payment processing method(e.g., credit card, or cryptocurrency such as bitcoin or another fortransferring payment). The rating at a particular spot can be viewed byother users, which rating can provide the other users an incentive topark at a particular spot.

In some embodiments, an autonomous vehicle (AV, or also referred to asSDV), in functioning as a second entity, may have a digital identifierlocation pertaining to a user associated with the AV and a transactioncan be conducted by the AV without the involvement of the user, forexample, by accessing a digital wallet of the user, and transacting witha first entity based on inspecting the unused time left at the specificparking location.

Another example usage scenario is described as follows with reference toan example commuter application. To reduce the need for parking in acity center, and to service car commuters who may not want to take theirvehicle into the city center, a park and ride lot external to the citycenter may provide parking spaces. Utilizing a methodology describedabove, unused durations may be transferred between a commuter's vehicleand a service vehicle such as a taxi, for instance, which may includeAVs. A taxi AV may occupy a space during evening hours or off hours. Asa commuter vehicle arrives at the park and ride lot in the morning, amethodology described above may negotiate the exchange of parking ratesand time from the taxi AV to the commuter vehicle. The taxi AV maysurrender the spot to the commuter user's vehicle, and the user mayengage and take the taxi AV in a single transaction. The taxi AV mayservice riders in the city center until called to return in the eveningto the park and ride lot where the exchange is made again, and the taxiAV parked for the evening. Such methodology may occur for multiple taxAVs occupying multiple parking spaces in a parking lot.

Yet as another example, a methodology described herein may be involvedin dynamic zoning of parking. For instance, utilizing local parkingzoning laws, a methodology may be able to provide contextualmodification of regulation to increase or decrease the number of parkingspots along traffic areas. For example, cities that have static parkingstart and end times in traffic areas can employ a methodology of thepresent disclosure, to increase the availability of parking spots and tocollect an optimized reward for a parking spot that was ineligible understatic zoning regulation. In this embodiment, the parking authoritycontrols parking spots that can be released for parking even before theofficial start time for parking is reached. This process is controlledvia a contextualized demand and supply that considers a need for parkingand a need to reduce traffic in congested areas. For instance, atransfer score value may be evaluated which combines inputs triggeredfrom a combination of geo-spatial and temporal metrics, one or morerules or regulations pertaining to a confined region, number of open andoccupied parking spots, and future desirable spots based on pricing. Acomputer executable component of functionality such as a zoning manager148 may implement managing of dynamic zoning of parking by taking thecontextualized demand and supply data from various processing modulesand data points (e.g., parking regulation data, real-time congestiondata from remote sensing data, IoT data, and/or others).

Still as another example, a methodology described herein may be involvedin ad hoc parking application. For instance, as SDVs and AVs are adoptedwidely, situations may occur in which these vehicles may be circling thestreets, even without occupants, in fully autonomous mode if a parkingspace cannot be found. Such situations may create a congestion and evenunsafe conditions. With a methodology of the present disclosure in oneembodiment, such vehicles that are seeking parking on their own may beallocated ad hoc parking locations by a municipality comprisingsurrounding normal usage of lanes in a road based on negotiation withthe SDVs using functions similar to negotiating for a parking space withunused duration. In this case, the municipality is negotiating a rightof way for temporary usage as parking. With respect to parking spacescreated by temporarily closing roads, different vehicles may communicatewith each other based on intended duration of stay at a particularparking spot, pricing, user context which, for example, may trigger aMessage Queuing Telemetry Transport (MQTT) enabled communicationprotocol (or another communication protocol) to initiate a negotiationto utilize a parking spot. Such communication may be performed based onuser configurable thresholds which may vary from one user to another. Insome embodiments, a zoning manager component or functionality 148 mayimplement this function. Examples of vehicles participating in thisexample scenario can be one or more of automated, self driven vehiclesand/or manually driven vehicles.

Broadcaster and receiver component or functionality 142 may broadcast ortransmit information to one or more second entities 106 in need of aparking space based on determination of profiles and contextualcharacteristics of one or more second entities. Receiver portion of thefunctionality may include receiving information related to unusedparking time left at specific locations based on proximity and cognitivestate of the second entities (e.g., SDVs or manual vehicles) in order totransmit the information to selected set of second entities overcommunication network and/or devices such as secure wireless network.Broadcaster portion of the functionality may transmit the information.In some aspects, other devices in proximal distance and having similargeo-spatial metrics (e.g., fetched from wearable devices, calendar dataand historical pattern analysis) may be notified of the availableparking space with the time left to be utilized to park. End-user andtelecommunication device manager component or functionality 146 maymanage data associated with different entities and communicationchannels

Considering the case of autonomous vehicles (AVs), in one embodiment,AVs can communicate with each other via a wireless interface moduleconnected to a centralized vehicle station and may be capable ofmonitoring each other's geo-spatial metrics and user's profileinformation related to the duration of stay and departure. Multiple AVscan coordinate with each other in order to readjust their positionsbased on time left at the meter and move onto other areas. AVs cancommunicate with parking meters in order to pay via associated users'digital currency and while departing from one spot, can indicate toanother AV in the vicinity which may be searching for a parking space ormay be arriving at the destination to take the parking space so thatunused time can be used in an efficient manner.

In one aspect, data, which is communicated between the components shownin FIG. 1, can be formatted into data packets, for example, havingstructures such as header information, payload, error detecting codesuch as a cyclic redundancy check (CRC) code, and/or others. In anotheraspect, the data may be encrypted and formatted into data packet frames.

FIG. 2 illustrates an example implementation of determining unusedduration in one embodiment. For instance, unused duration estimator 112shown in FIG. 1 may include an artificial neural network, which may betrained to predict an unused duration and, for example, also compute atransfer score associated with a second entity. An artificial neuralnetwork can include multiple layers and a layer can include multiplenodes (also referred to as neurons). Nodes of layers can beinterconnected in succession, e.g., nodes of a layer interconnected tonodes of a next layer in the network. Output values or signals from thenodes in one layer can be weighted and transmitted to nodes in the nextlayer. A neural network may be multi-level, and may include an inputlayer 202, one or more intermediate layers (also referred to as hiddenlayers) 204 and an output layer (shown as part of 204). Weights can beassigned to a node-node connection, for instance, such that a signaltransmitted from a source node to a target node is weighted by theassigned weight between the two nodes. Input layer 202 may include anumber of nodes representing input features, which are input to predictunused duration. A weight may be assigned to each input feature and canbe reconfigured based on importance given to each input feature dataset. Nodes in the intermediate layers may receive weighted signals fromthe interconnected previous layer, and generate an output signal basedon executing an activation function using the received weighted signals.The generated output signal then may be transmitted to a node of a nextlayer. An example training may include using a backpropagation techniqueand training the neural network can adjust the weights. In someembodiments, reinforcement learning feedback is used to fortify orconfigure the weights assigned to the initial input features as shown at206. In some embodiments, a neural network can be implemented on specialhardware such as a field programmable gate array (FPGA) or anotherprocessor implemented for neural network configuration. For instance, aspecial hardware may include memory cells or blocks that store theweights and a logic block or circuitry implementing the artificialneural network computations. In one aspect, the neural network may traina classifier. For instance, the trained neural network model may outputa classification. The computed or predicted unused duration can bebroadcasted to a second entity, or to a selected second entity which maybe selected based on the computed transfer score.

Input features in the input layer 202 may include, but are not limitedto, contextual situation, first entity's social causality and behavioralfactors (which may include geo-spatial metrics and temporal metrics),used parking time analysis, and duration of stay at a location.Contextual situation may include current engagement or meeting such asreal-time information obtained from Internet Protocol (IP) cameras andmobile devices, which may also include information associated withevents occurring within a threshold distance of the parking space.Social causality may include information obtained based on patternhistory of users such as social media information. Such user informationmay be obtained based on user opting-in to allow access to suchinformation. For instance, natural language processing andclassification techniques may be implemented to perform keywordextraction for estimating data such as the length of a meeting andadjusting weights to different data inputs. Used parking time analysismay include monitoring and/or determining the length of time paid forand left at the current location, for example, 30 minutes time is overout of 1 full paid hour. Duration of stay at a confined location mayspecify the total time (e.g., estimated or predicted) at the location avehicle may stay in terms of time such as hours or minutes, based on oneor more events such as contextual situation. One or more layers at 204may include nodes, each of which may compute an activation functionbased on the input. Weights of the nodes may be adjusted via a backpropagation technique, in training a neural network model for predictingunused duration. The neural network may be also trained to compute orpredict a transfer score, for example, based on the predicted unusedduration, contextual situation (e.g., input feature) and the firstentity's social causality and behavioral factors (e.g., input feature).In some embodiments, based on the transfer score computation, the unusedtime may be transmitted and payment can be processed at 208. Userfeedback may be received at 210, based on which weights of the inputfeatures can be adjusted. The adjusted weights can be fed into the inputlayer 202.

FIG. 3 is a flow diagram illustrating a method in one embodiment. Themethod, for instance, transfers predicted unused duration associatedwith a parking space, for instance, predicted using a trained machinelearning model, for example, an artificial neural network model. At 302,availability of unused duration of a parking space is predicted for asecond entity. Predicting may include executing a computer-implemented,artificial neural network trained to predict the availability of unusedduration of a parking space. Such artificial neural network, forinstance, may have been trained based on input features, which mayinclude but are not limited to a contextual situation associated withthe second entity, a behavior factor associated with a first entity thathas been using the parking space, geographical location and time, eventsoccurring within a threshold distance from the parking space. Thetrained artificial neural network may also output a transfer affinity ortransfer score based on the predicted availability of the unusedduration, the contextual situation associated with the second entity andthe behavior factor associated with the first entity. For instance, theartificial neural network may have been further trained to predict atransfer affinity based on historical data which may includeavailability of the unused duration, contextual situation associatedwith the second entity and behavior factor associated with the firstentity.

At 304, based at least on the transfer affinity, the second entity maybe selected. At 306, the unused duration can be transferred to thesecond entity from the first entity. For instance, a data packet may betransmitted to the second entity, which data packet may includecommunication header information and payload information indicating theavailability of the unused duration. Such data packet may be transmittedover a communication network. The payload information may include anotification and/or the amount of the unused duration. The payloadinformation of the data packet may also include other data associatedwith the parking space, for example, an image of the parking space,inferred characteristic associated with the parking space and/or otherdata. Transferring may also include storing a payment and associatedcomputation as a blockchain node in a blockchain. In some embodiments,the first entity and the second entity comprise automated self-drivingvehicles. In some embodiments, the first entity and the second entity,which are automated self-driving vehicles, can automatically exchangethe unused duration and the payment without human input. In someembodiments, the method may also include communication trafficcongestion data associated with an area, detected by at least one of thefirst entity and the second entity, to a traffic authority, for example,to allow the traffic authority to initiate creating dynamically oftemporary parking spaces based on the congestion data. In someembodiment, another artificial intelligence model may be trained, whichcan predict whether the second entity is heading to the parking space.The artificial intelligence model may be trained based on historic dataassociated with travel patterns of the second entity. Based on aprediction that the second entity is heading to the parking space,predicting of the availability of unused duration of a parking space canbe triggered.

FIG. 4 is a diagram showing components of a system in one embodiment,which may implement parking continuity apparatus and/or method. One ormore hardware processors 402 such as a central processing unit (CPU), agraphic process unit (GPU), and/or a Field Programmable Gate Array(FPGA), an application specific integrated circuit (ASIC), and/oranother processor, may be coupled with a memory device 404, and predictunused parking duration availability. The memory device may includerandom access memory (RAM), read-only memory (ROM) or another memorydevice, and may store data and/or processor instructions forimplementing various functionalities associated with the methods and/orsystems described herein. The processor may execute computerinstructions stored in the memory or received from another computerdevice or medium. The memory device 404 may, for example, storeinstructions and/or data for functioning of one or more hardwareprocessors 402, and may include an operating system and other program ofinstructions and/or data. One or more hardware processors 402 ordifferent one or more hardware processor may perform training of aneural network model, which can predict unused duration associated witha parking space. Such a model may be stored in the memory device 404,and/or a storage device 406, and/or may be received via a networkinterface 408 from another processor or computer system, for example,for execution by one or more hardware processors 402. Otherconfigurations for storing the model are possible. One or more hardwareprocessors 402 may also initiate communications between an automatedvehicle searching for a parking space and another automated vehiclegiving up a parking space. One or more hardware processors 402 may becoupled with interface devices such as a network interface 408 forcommunicating with remote systems, for example, via a network, and aninput/output interface 410 for communicating with input and/or outputdevices such as a keyboard, mouse, display, and/or others.

FIG. 5 illustrates a schematic of an example computer or processingsystem that may implement a system in one embodiment of the presentdisclosure. The computer system is only one example of a suitableprocessing system and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the methodologydescribed herein. The processing system shown may be operational withnumerous other general purpose or special purpose computing systemenvironments or configurations. Examples of well-known computingsystems, environments, and/or configurations that may be suitable foruse with the processing system shown in FIG. 5 may include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

We claim:
 1. A computer-implemented method, comprising: predictingavailability of an unused duration of a parking space for a secondentity, the predicting comprising executing a computer-implemented,artificial neural network trained to predict the availability of anunused duration of a parking space based on input features comprising atleast a contextual situation associated with the second entity, abehavior factor associated with a first entity that has been using theparking space, geographical location and time, events occurring within athreshold distance from the parking space, the artificial neural networkfurther trained to output a transfer affinity based on the predictedavailability of an unused duration, the contextual situation associatedwith the second entity and the behavior factor associated with the firstentity, the artificial neural network trained to predict theavailability of the unused duration of the parking space predicting thatthe first entity is leaving the parking space so as to result in theunused duration; based at least on the transfer affinity, selecting thesecond entity; and transferring the unused duration to the second entityfrom the first entity, the transferring further comprising storing apayment and associated computation as a blockchain node in a blockchain,wherein the first entity and the second entity comprise automatedself-driving vehicles and the first entity is allowed to exchange datafor allowing the second entity in making a real-time decision, the dataincluding at least a video stream, characteristic of the parking spaceincluding existence of puddles on the driver's side, and time sensitiveinformation associated with the parking space, wherein the second entityis further operable to automatically detect that the parking space istemporarily unavailable for parking and in response autonomously drivearound area of the parking space and return after an amount of time. 2.The computer-implemented method of claim 1, wherein the transferringcomprises transmitting a data packet to the second entity over acommunication network, the data packet comprising header information andpayload information indicating the unused duration.
 3. Thecomputer-implemented method of claim 2, wherein the payload informationin the data packet further comprises data associated with the parkingspace comprising at least an image of the parking space.
 4. Thecomputer-implemented method of claim 2, wherein the payload informationin the data packet further comprises data associated with the parkingspace comprising at least an inferred characteristic associated with theparking space.
 5. The computer-implemented method of claim 1, furthercomprising communicating traffic congestion data associated with anarea, detected by at least one of the first entity and the second entityto a traffic authority, to allow the traffic authority to initiatecreating dynamically of temporary parking spaces based on the congestiondata.
 6. The computer-implemented method of claim 1, wherein the firstentity and the second entity automatically exchange the unused durationand the payment.
 7. The computer-implemented method of claim 1, furthercomprising training an artificial intelligence model that predictswhether the second entity is heading to the parking space, theartificial intelligence model trained based on historic data associatedwith travel patterns of the second entity, and based on a predictionthat the second entity is heading to the parking space, triggering thepredicting of the availability of unused duration of a parking space. 8.A system, comprising: at least one hardware processor; a memory devicecoupled with the at least one hardware processor; and a communicationinterface coupled to the at least one hardware processor; the at leastone hardware processor operable to at least perform: predictingavailability of an unused duration of a parking space for a secondentity, the predicting comprising executing a computer-implemented,artificial neural network trained to predict the availability of anunused duration of a parking space based on input features comprising atleast a contextual situation associated with the second entity, abehavior factor associated with a first entity that has been using theparking space, geographical location and time, events occurring within athreshold distance from the parking space, the artificial neural networkfurther trained to output a transfer affinity based on the predictedavailability of an unused duration, the contextual situation associatedwith the second entity and the behavior factor associated with the firstentity, predicting the availability of the unused duration of theparking space including predicting that the first entity is leaving theparking space so as to result in the unused duration; based at least onthe transfer affinity, selecting the second entity; and transferring theunused duration to the second entity from the first entity, thetransferring further comprising storing a payment and associatedcomputation as a blockchain node in a blockchain, wherein the firstentity and the second entity comprise automated self-driving vehiclesand the first entity is allowed to exchange data for allowing the secondentity in making a real-time decision, the data including at least avideo stream, characteristic of the parking space including existence ofpuddles on the driver's side, and time sensitive information associatedwith the parking space, wherein the second entity is further operable toautomatically detect that the parking space is temporarily unavailablefor parking and in response autonomously drive around area of theparking space and return after an amount of time.
 9. The system of claim8, wherein the at least one hardware processor transferring the unusedduration comprises transmitting a data packet to the second entity overa communication network, the data packet comprising header informationand payload information indicating the unused duration.
 10. The systemof claim 9, wherein the payload information in the data packet furthercomprises data associated with the parking space comprising at least animage of the parking space.
 11. The system of claim 9, wherein thepayload information in the data packet further comprises data associatedwith the parking space comprising at least an inferred characteristicassociated with the parking space.
 12. The system of claim 8, furthercomprising communicating traffic congestion data associated with anarea, detected by at least one of the first entity and the second entityto a traffic authority, to allow the traffic authority to initiatecreating dynamically of temporary parking spaces based on the congestiondata.
 13. The system of claim 8, wherein the first entity and the secondentity automatically exchange the unused duration and the payment. 14.The system of claim 8, further comprising training an artificialintelligence model that predicts whether the second entity is heading tothe parking space, the artificial intelligence model trained based onhistoric data associated with travel patterns of the second entity, andbased on a prediction that the second entity is heading to the parkingspace, triggering the predicting of the availability of unused durationof a parking space.
 15. A computer readable storage medium storing aprogram of instructions executable by a machine to perform a method,comprising: predicting availability of an unused duration of a parkingspace for a second entity, the predicting comprising executing acomputer-implemented, artificial neural network trained to predict theavailability of an unused duration of a parking space based on inputfeatures comprising at least a contextual situation associated with thesecond entity, a behavior factor associated with a first entity that hasbeen using the parking space, geographical location and time, eventsoccurring within a threshold distance from the parking space, theartificial neural network further trained to output a transfer affinitybased on the predicted availability of an unused duration, thecontextual situation associated with the second entity and the behaviorfactor associated with the first entity, the artificial neural networktrained to predict the availability of the unused duration of theparking space predicting that the first entity is leaving the parkingspace so as to result in the unused duration; based at least on thetransfer affinity, selecting the second entity; and transferring theunused duration to the second entity from the first entity, thetransferring further comprising storing a payment and associatedcomputation as a blockchain node in a blockchain, wherein the firstentity and the second entity comprise automated self-driving vehiclesand the first entity is allowed to exchange data for allowing the secondentity in making a real-time decision, the data including at least avideo stream, characteristic of the parking space including existence ofpuddles on the driver's side, and time sensitive information associatedwith the parking space, wherein the second entity is further operable toautomatically detect that the parking space is temporarily unavailablefor parking and in response autonomously drive around area of theparking space and return after an amount of time.
 16. The computerreadable storage medium of claim 15, wherein the transferring comprisestransmitting a data packet to the second entity over a communicationnetwork, the data packet comprising header information and payloadinformation indicating the unused duration.
 17. The computer readablestorage medium of claim 16, wherein the payload information in the datapacket further comprises data associated with the parking spacecomprising at least one of an image of the parking space and inferredcharacteristic associated with the parking space.